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Computer assisted structure–taste studies on sulfamates by pattern recognition method using graph theoretical invariants
Author(s) -
Okuyama Tohru,
Miyashita Yoshikatsu,
Kanaya Shigehiko,
Katsumi Hiroyuki,
Sasaki ShinIchi,
Randić Milan
Publication year - 1988
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540090609
Subject(s) - substituent , graph , vertex (graph theory) , path (computing) , mathematics , chemistry , combinatorics , pattern recognition (psychology) , stereochemistry , artificial intelligence , computer science , programming language
Structure–taste relationships for 25 acyclic and 20 cyclic carbosulfamates were investigated by means of pattern recognition using different graph theoretical invariants as molecular substituent descriptors. The SIMCA method was used to classify the compounds into sweet and nonsweet classes. All selected graph theoretical invariants that are related to the “rooted” vertex were found to give promising results. Using the weighted path numbers and self‐returning walks for the rooted atom as descriptors of substituents, we found 87% of acyclic compounds were correctly classified. Using the atomic path numbers for the rooted atom as descriptors of substituents, we found 81% of cyclic compounds were correctly classified. These results are better than previously used shape and size substituent descriptors. It may be concluded that the graph theoretical descriptors have great potential in encoding structure components in structure–activity studies (SAR) studies.